DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer

Abstract

Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab's mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.

References in corpus (3)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Going Deeper with Convolutions

Cited by in corpus (12)

• Deep Learning for Logo Recognition
• A Deep One-Shot Network for Query-based Logo Retrieval
• FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network
• Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
• Deep Learning Logo Detection with Data Expansion by Synthesising Context
• Scalable Deep Learning Logo Detection
• LogoDet-3K: A Large-Scale Image Dataset for Logo Detection
• Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification
• Large Scale Open-Set Deep Logo Detection
• Image Matters: Scalable Detection of Offensive and Non-Compliant Content / Logo in Product Images
• Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection
• Video Logo Retrieval based on local Features